Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation
<p dir="ltr">Accelerated brain aging and abnormalities are associated with variations in brain patterns. Effective and reliable assessment methods are required to accurately classify and estimate brain age. In this study, a brain age classification and estimation framework is propose...
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2024
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| _version_ | 1864513545032433664 |
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| author | Raveendra Pilli (21633287) |
| author2 | Tripti Goel (21633290) R. Murugan (21633293) M. Tanveer (1758181) P. N. Suganthan (21633296) |
| author2_role | author author author author |
| author_facet | Raveendra Pilli (21633287) Tripti Goel (21633290) R. Murugan (21633293) M. Tanveer (1758181) P. N. Suganthan (21633296) |
| author_role | author |
| dc.creator.none.fl_str_mv | Raveendra Pilli (21633287) Tripti Goel (21633290) R. Murugan (21633293) M. Tanveer (1758181) P. N. Suganthan (21633296) |
| dc.date.none.fl_str_mv | 2024-01-18T21:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/tcds.2024.3349593 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Kernel-Ridge-Regression-Based_Randomized_Network_for_Brain_Age_Classification_and_Estimation/29445857 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Neurosciences Engineering Biomedical engineering Cerebrospinal fluid (CSF) gray matter (GM) kernel ridge regression-random vector functional link (KRR-RVFL) magnetic resonance imaging (MRI) white matter (WM) Aging Feature extraction Magnetic resonance imaging Kernel Convolutional neural networks Brain modeling Standards |
| dc.title.none.fl_str_mv | Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Accelerated brain aging and abnormalities are associated with variations in brain patterns. Effective and reliable assessment methods are required to accurately classify and estimate brain age. In this study, a brain age classification and estimation framework is proposed using structural magnetic resonance imaging (sMRI) scans, a 3-D convolutional neural network (3-D-CNN), and a kernel ridge regression-based random vector functional link (KRR-RVFL) network. We used 480 brain MRI images from the publicly availabel IXI database and segmented them into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) images to show age-related associations by region. Features from MRI images are extracted using 3-D-CNN and fed into the wavelet KRR-RVFL network for brain age classification and prediction. The proposed algorithm achieved high classification accuracy, 97.22%, 99.31%, and 95.83% for GM, WM, and CSF regions, respectively. Moreover, the proposed algorithm demonstrated excellent prediction accuracy with a mean absolute error (MAE) of <b>3.89</b> years, <b>3.64 </b>years, and <b>4.49</b> years for GM, WM, and CSF regions, confirming that changes in WM volume are significantly associated with normal brain aging. Additionally, voxel-based morphometry (VBM) examines age-related anatomical alterations in different brain regions in GM, WM, and CSF tissue volumes.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Transactions on Cognitive and Developmental Systems<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tcds.2024.3349593" target="_blank">https://dx.doi.org/10.1109/tcds.2024.3349593</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_2025cb60910f21713a06b2fd25cbe1e8 |
| identifier_str_mv | 10.1109/tcds.2024.3349593 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29445857 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and EstimationRaveendra Pilli (21633287)Tripti Goel (21633290)R. Murugan (21633293)M. Tanveer (1758181)P. N. Suganthan (21633296)Biomedical and clinical sciencesClinical sciencesNeurosciencesEngineeringBiomedical engineeringCerebrospinal fluid (CSF)gray matter (GM)kernel ridge regression-random vector functional link (KRR-RVFL)magnetic resonance imaging (MRI)white matter (WM)AgingFeature extractionMagnetic resonance imagingKernelConvolutional neural networksBrain modelingStandards<p dir="ltr">Accelerated brain aging and abnormalities are associated with variations in brain patterns. Effective and reliable assessment methods are required to accurately classify and estimate brain age. In this study, a brain age classification and estimation framework is proposed using structural magnetic resonance imaging (sMRI) scans, a 3-D convolutional neural network (3-D-CNN), and a kernel ridge regression-based random vector functional link (KRR-RVFL) network. We used 480 brain MRI images from the publicly availabel IXI database and segmented them into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) images to show age-related associations by region. Features from MRI images are extracted using 3-D-CNN and fed into the wavelet KRR-RVFL network for brain age classification and prediction. The proposed algorithm achieved high classification accuracy, 97.22%, 99.31%, and 95.83% for GM, WM, and CSF regions, respectively. Moreover, the proposed algorithm demonstrated excellent prediction accuracy with a mean absolute error (MAE) of <b>3.89</b> years, <b>3.64 </b>years, and <b>4.49</b> years for GM, WM, and CSF regions, confirming that changes in WM volume are significantly associated with normal brain aging. Additionally, voxel-based morphometry (VBM) examines age-related anatomical alterations in different brain regions in GM, WM, and CSF tissue volumes.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Transactions on Cognitive and Developmental Systems<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tcds.2024.3349593" target="_blank">https://dx.doi.org/10.1109/tcds.2024.3349593</a></p>2024-01-18T21:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/tcds.2024.3349593https://figshare.com/articles/journal_contribution/Kernel-Ridge-Regression-Based_Randomized_Network_for_Brain_Age_Classification_and_Estimation/29445857CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/294458572024-01-18T21:00:00Z |
| spellingShingle | Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation Raveendra Pilli (21633287) Biomedical and clinical sciences Clinical sciences Neurosciences Engineering Biomedical engineering Cerebrospinal fluid (CSF) gray matter (GM) kernel ridge regression-random vector functional link (KRR-RVFL) magnetic resonance imaging (MRI) white matter (WM) Aging Feature extraction Magnetic resonance imaging Kernel Convolutional neural networks Brain modeling Standards |
| status_str | publishedVersion |
| title | Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation |
| title_full | Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation |
| title_fullStr | Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation |
| title_full_unstemmed | Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation |
| title_short | Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation |
| title_sort | Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation |
| topic | Biomedical and clinical sciences Clinical sciences Neurosciences Engineering Biomedical engineering Cerebrospinal fluid (CSF) gray matter (GM) kernel ridge regression-random vector functional link (KRR-RVFL) magnetic resonance imaging (MRI) white matter (WM) Aging Feature extraction Magnetic resonance imaging Kernel Convolutional neural networks Brain modeling Standards |